Results: Twenty-three studies were included in the final review. Thirty potentially modifiable determinants across seven domains (oral, psychosocial, medication and care, health, physical function, lifestyle, eating) were included. The majority of studies had a high risk of bias and were of a low quality. There is moderate evidence that hospitalisation, eating dependency, poor self-perceived health, poor physical function and poor appetite are determinants of malnutrition. Moderate evidence suggests that chewing difficulties, mouth pain, gum issues co-morbidity, visual and hearing impairments, smoking status, alcohol consumption and physical activity levels, complaints about taste of food and specific nutrient intake are not determinants of malnutrition. There is low evidence that loss of interest in life, access to meals and wheels, and modified texture diets are determinants of malnutrition. Furthermore, there is low evidence that psychological distress, anxiety, loneliness, access to transport and wellbeing, hunger and thirst are not determinants of malnutrition. There appears to be conflicting evidence that dental status, swallowing, cognitive function, depression, residential status, medication intake and/or polypharmacy, constipation, periodontal disease are determinants of malnutrition. Conclusion: There are multiple potentially modifiable determinants of malnutrition however strong robust evidence is lacking for the majority of determinants. Better prospective cohort studies are required. With an increasingly ageing population, targeting modifiable factors will be crucial to the effective treatment and prevention of malnutrition.

Backgroundscreening for cognitive impairment in Emergency Department (ED) requires short, reliable tools.Objectiveto validate the 4AT and 6-Item Cognitive Impairment Test (6-CIT) for ED dementia and delirium screening.Designdiagnostic accuracy study.Setting/subjectsattendees aged ≥70 years in a tertiary care hospital’s ED.Methodstrained researchers assessed participants using the Standardised Mini Mental State Examination, Delirium Rating Scale-Revised 98 and Informant Questionnaire on Cognitive Decline in the Elderly, informing ultimate expert diagnosis using Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia and delirium (reference standards). Another researcher blindly screened each participant, within 3 h, using index tests 4AT and 6-CIT.Resultof 419 participants (median age 77 years), 15.2% had delirium and 21.5% had dementia. For delirium detection, 4AT had positive predictive value (PPV) 0.68 (95% confidence intervals: 0.58–0.79) and negative predictive value (NPV) 0.99 (0.97–1.00). At a pre-specified 9/10 cut-off (9 is normal), 6-CIT had PPV 0.35 (0.27–0.44) and NPV 0.98 (0.95–0.99).Importantly, 52% of participants had no family present. A novel algorithm for scoring 4AT item 4 where collateral history is unavailable (score 4 if items 2–3 score ≥1; score 0 if items 1–3 score is 0) proved reliable; PPV 0.65 (0.54–0.76) and NPV 0.99 (0.97–1.00). For dementia detection, 4AT had PPV 0.39 (0.32–0.46) and NPV 0.94 (0.89–0.96); 6-CIT had PPV 0.46 (0.37–0.55) and NPV 0.94 (0.90–0.97).Conclusion6-CIT and 4AT accurately exclude delirium and dementia in older ED attendees. 6-CIT does not require collateral history but has lower PPV for delirium.

Wrist-worn activity trackers have experienced a tremendous growth lately and studies on the accuracy of mainstream trackers used by older adults are needed. This study explores the performance of six trackers (Fitbit Charge2, Garmin VivoSmart HR+, Philips Health Watch, Withings Pulse Ox, ActiGraph GT9X-BT, Omron HJ-72OITC) for estimating: steps, travelled distance, and heart-rate measurements for a cohort of older adults. Eighteen older adults completed a structured protocol involving walking tasks, simulated household activities, and sedentary activities. Less standardized activities were also included, such as: dusting, using a walking aid, or playing cards, in order to simulate real-life scenarios. Wrist-mounted and chest/waist-mounted devices were used. Gold-standards included treadmill, ECG-based chest strap, direct observation or video recording according to the activity and parameter. Every tracker showed a decreasing accuracy with slower walking speed, which resulted in a significant step under-counting. A large mean absolute percentage error (MAPE) was found for every monitor at slower walking speeds with the lowest reported MAPE at 2 km/h being 7.78%, increasing to 20.88% at 1.5 km/h, and 44.53% at 1 km/h. During household activities, the MAPE climbing up/down-stairs ranged from 8.38–19.3% and 10.06–19.01% (dominant and non-dominant arm), respectively. Waist-worn devices showed a more uniform performance. However, unstructured activities (e.g. dusting, playing cards), and using a walking aid represent a challenge for all wrist-worn trackers as evidenced by large MAPE (> 57.66% for dusting, > 67.32% when using a walking aid). Poor performance in travelled distance estimation was also evident during walking at low speeds and climbing up/down-stairs (MAPE > 71.44% and > 48.3%, respectively). Regarding heart-rate measurement, there was no significant difference (p-values > 0.05) in accuracy between trackers placed on the dominant or non-dominant arm. Concordant with existing literature, while the mean error was limited (between -3.57 bpm and 4.21 bpm), a single heart-rate measurement could be underestimated up to 30 beats-per-minute. This study showed a number of limitations of consumer-level wrist-based activity trackers for older adults. Therefore caution is required when used, in healthcare or in research settings, to measure activity in older adults.

Parkinson’s disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient’s condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients’ status and the disease’s symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson’s, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms’ assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.

Background Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. Objective This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. Methods We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. Results In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life. Conclusions The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences.